Overview

Dataset statistics

Number of variables12
Number of observations950
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.2 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

amperestunden is highly correlated with zyklus_ and 5 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 4 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 3 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 3 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 3 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 6 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 6 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 4 other fieldsHigh correlation
time_pause_vorher is highly correlated with amperestunden and 5 other fieldsHigh correlation
time_temp_hoch is highly correlated with temperature_amax and 3 other fieldsHigh correlation
time_temp_hoch_vorher is highly correlated with temperature_amax and 7 other fieldsHigh correlation
amperestunden is highly correlated with zyklus_ and 4 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 2 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 1 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 1 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 1 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 5 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 2 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 2 other fieldsHigh correlation
time_temp_hoch is highly correlated with time_temp_hoch_vorherHigh correlation
time_temp_hoch_vorher is highly correlated with time_temp_hochHigh correlation
amperestunden is highly correlated with zyklus_ and 4 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 3 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 2 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 2 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 2 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 4 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 4 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 2 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 3 other fieldsHigh correlation
time_pause_vorher is highly correlated with time_amin and 1 other fieldsHigh correlation
time_temp_hoch is highly correlated with temperature_amax and 2 other fieldsHigh correlation
amperestunden is highly correlated with zyklus_ and 6 other fieldsHigh correlation
zyklus_ is highly correlated with amperestunden and 7 other fieldsHigh correlation
temperature_amax is highly correlated with temperature_amin and 1 other fieldsHigh correlation
temperature_amin is highly correlated with temperature_amax and 2 other fieldsHigh correlation
temperature_mean is highly correlated with temperature_amax and 1 other fieldsHigh correlation
time_amin is highly correlated with amperestunden and 7 other fieldsHigh correlation
time_entladen_stark_vorher is highly correlated with amperestunden and 7 other fieldsHigh correlation
time_entladen_leicht_vorher is highly correlated with amperestunden and 7 other fieldsHigh correlation
time_laden_stark_vorher is highly correlated with amperestunden and 6 other fieldsHigh correlation
time_pause_vorher is highly correlated with zyklus_ and 5 other fieldsHigh correlation
time_temp_hoch is highly correlated with amperestunden and 5 other fieldsHigh correlation
time_temp_hoch_vorher is highly correlated with amperestunden and 8 other fieldsHigh correlation
time_amin has unique values Unique
time_entladen_leicht_vorher has unique values Unique
time_laden_stark_vorher has unique values Unique
time_entladen_stark_vorher has 40 (4.2%) zeros Zeros
time_pause_vorher has 22 (2.3%) zeros Zeros
time_temp_hoch has 593 (62.4%) zeros Zeros
time_temp_hoch_vorher has 33 (3.5%) zeros Zeros

Reproduction

Analysis started2021-12-23 13:33:08.851870
Analysis finished2021-12-23 13:33:32.809435
Duration23.96 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

amperestunden
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct948
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5014888
Minimum0
Maximum2.147192722
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:32.943169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.011283349
Q11.253411514
median1.491576156
Q31.754460949
95-th percentile2.043836863
Maximum2.147192722
Range2.147192722
Interquartile range (IQR)0.5010494347

Descriptive statistics

Standard deviation0.3325745011
Coefficient of variation (CV)0.2214964914
Kurtosis0.2232667832
Mean1.5014888
Median Absolute Deviation (MAD)0.2526541546
Skewness-0.2003476861
Sum1426.41436
Variance0.1106057988
MonotonicityNot monotonic
2021-12-23T14:33:33.070278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.3%
1.7819399091
 
0.1%
1.7467561541
 
0.1%
2.0089837571
 
0.1%
2.0029339881
 
0.1%
1.9434059371
 
0.1%
1.943111551
 
0.1%
1.8632531711
 
0.1%
1.864670051
 
0.1%
1.8012449121
 
0.1%
Other values (938)938
98.7%
ValueCountFrequency (%)
03
0.3%
0.69297840441
 
0.1%
0.74877821141
 
0.1%
0.7502029931
 
0.1%
0.7587395321
 
0.1%
0.75977552551
 
0.1%
0.77836437461
 
0.1%
0.78493920361
 
0.1%
0.81164762651
 
0.1%
0.81683376491
 
0.1%
ValueCountFrequency (%)
2.1471927221
0.1%
2.1415824041
0.1%
2.1388317841
0.1%
2.1374373551
0.1%
2.1369088381
0.1%
2.1348230391
0.1%
2.1346482831
0.1%
2.1339161521
0.1%
2.1326148161
0.1%
2.1319957931
0.1%

zyklus_
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28218.97053
Minimum1
Maximum113576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:33.198181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile865.8
Q17952.75
median16073
Q339511.25
95-th percentile93463.65
Maximum113576
Range113575
Interquartile range (IQR)31558.5

Descriptive statistics

Standard deviation28321.90788
Coefficient of variation (CV)1.003647807
Kurtosis0.8231802114
Mean28218.97053
Median Absolute Deviation (MAD)11970.5
Skewness1.327962722
Sum26808022
Variance802130466
MonotonicityNot monotonic
2021-12-23T14:33:33.328313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219
 
2.0%
38
 
0.8%
55
 
0.5%
14
 
0.4%
242094
 
0.4%
30314
 
0.4%
30334
 
0.4%
60574
 
0.4%
90874
 
0.4%
121114
 
0.4%
Other values (851)890
93.7%
ValueCountFrequency (%)
14
 
0.4%
219
2.0%
38
0.8%
43
 
0.3%
55
 
0.5%
91
 
0.1%
401
 
0.1%
472
 
0.2%
8541
 
0.1%
8561
 
0.1%
ValueCountFrequency (%)
1135761
0.1%
1135721
0.1%
1108171
0.1%
1105551
0.1%
1105511
0.1%
1093871
0.1%
1091991
0.1%
1091951
0.1%
1078001
0.1%
1077961
0.1%

temperature_amax
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct810
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-343.1336119
Minimum-4099.44775
Maximum44.93351
Zeros0
Zeros (%)0.0%
Negative90
Negative (%)9.5%
Memory size7.5 KiB
2021-12-23T14:33:33.470190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile-4093.927
Q124.64716
median27.059845
Q334.33386
95-th percentile43.01202
Maximum44.93351
Range4144.38126
Interquartile range (IQR)9.6867

Descriptive statistics

Standard deviation1183.820648
Coefficient of variation (CV)-3.450028231
Kurtosis6.184014818
Mean-343.1336119
Median Absolute Deviation (MAD)3.35148
Skewness-2.858384352
Sum-325976.9313
Variance1401431.326
MonotonicityNot monotonic
2021-12-23T14:33:33.787669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92748
 
5.1%
-4094.0981434
 
3.6%
24.435814
 
0.4%
24.337483
 
0.3%
24.461823
 
0.3%
23.673163
 
0.3%
24.26463
 
0.3%
27.257823
 
0.3%
23.315183
 
0.3%
43.093192
 
0.2%
Other values (800)844
88.8%
ValueCountFrequency (%)
-4099.447752
 
0.2%
-4094.0981434
3.6%
-4093.92748
5.1%
-4059.53762
 
0.2%
-70.10111
 
0.1%
-63.095591
 
0.1%
-57.931511
 
0.1%
-54.885531
 
0.1%
22.080741
 
0.1%
22.647441
 
0.1%
ValueCountFrequency (%)
44.933511
0.1%
44.771311
0.1%
44.750111
0.1%
44.666741
0.1%
44.433381
0.1%
44.326771
0.1%
44.278151
0.1%
44.099661
0.1%
44.08311
0.1%
44.077641
0.1%

temperature_amin
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct807
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-369.2755797
Minimum-4099.44775
Maximum40.65417
Zeros0
Zeros (%)0.0%
Negative91
Negative (%)9.6%
Memory size7.5 KiB
2021-12-23T14:33:33.923231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile-4093.927
Q119.4810675
median21.80882
Q328.5679825
95-th percentile38.9023835
Maximum40.65417
Range4140.10192
Interquartile range (IQR)9.086915

Descriptive statistics

Standard deviation1212.788638
Coefficient of variation (CV)-3.284237314
Kurtosis5.580768786
Mean-369.2755797
Median Absolute Deviation (MAD)3.336185
Skewness-2.751126451
Sum-350811.8007
Variance1470856.28
MonotonicityNot monotonic
2021-12-23T14:33:34.043027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92752
 
5.5%
-4094.0981434
 
3.6%
-4099.447753
 
0.3%
28.662853
 
0.3%
21.067483
 
0.3%
19.52993
 
0.3%
20.989653
 
0.3%
19.718283
 
0.3%
19.166853
 
0.3%
19.025372
 
0.2%
Other values (797)841
88.5%
ValueCountFrequency (%)
-4099.447753
 
0.3%
-4094.0981434
3.6%
-4093.92752
5.5%
-4059.53762
 
0.2%
16.498121
 
0.1%
17.16611
 
0.1%
17.383541
 
0.1%
17.50441
 
0.1%
17.532011
 
0.1%
17.555591
 
0.1%
ValueCountFrequency (%)
40.654171
0.1%
40.641241
0.1%
40.563911
0.1%
40.562791
0.1%
40.410311
0.1%
40.321741
0.1%
40.242421
0.1%
40.213081
0.1%
40.119251
0.1%
39.87411
0.1%

temperature_mean
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct870
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-362.7266643
Minimum-4099.44775
Maximum42.14114416
Zeros0
Zeros (%)0.0%
Negative91
Negative (%)9.6%
Memory size7.5 KiB
2021-12-23T14:33:34.166836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4099.44775
5-th percentile-4093.927
Q122.3161411
median24.4982451
Q331.68772172
95-th percentile41.02405727
Maximum42.14114416
Range4141.588894
Interquartile range (IQR)9.371580623

Descriptive statistics

Standard deviation1206.597105
Coefficient of variation (CV)-3.326463765
Kurtosis5.690477797
Mean-362.7266643
Median Absolute Deviation (MAD)3.192067399
Skewness-2.770344446
Sum-344590.331
Variance1455876.574
MonotonicityNot monotonic
2021-12-23T14:33:34.285082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4093.92739
 
4.1%
-4094.0981429
 
3.1%
-4093.9279
 
0.9%
-4094.098145
 
0.5%
-4059.53762
 
0.2%
-4099.447752
 
0.2%
23.911831811
 
0.1%
41.207987251
 
0.1%
41.016482791
 
0.1%
41.503607341
 
0.1%
Other values (860)860
90.5%
ValueCountFrequency (%)
-4099.447752
 
0.2%
-4094.098145
 
0.5%
-4094.0981429
3.1%
-4093.9279
 
0.9%
-4093.92739
4.1%
-4086.8160111
 
0.1%
-4059.53762
 
0.2%
-4036.990361
 
0.1%
-4004.9628011
 
0.1%
-3925.2898771
 
0.1%
ValueCountFrequency (%)
42.141144161
0.1%
42.117619871
0.1%
42.03270261
0.1%
41.916006231
0.1%
41.823378261
0.1%
41.816040311
0.1%
41.808441051
0.1%
41.754365611
0.1%
41.730017961
0.1%
41.697681481
0.1%

time_amin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6860446.252
Minimum1633.73
Maximum17577173.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:34.405575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1633.73
5-th percentile444543.7585
Q13126158.735
median6566721.22
Q310263763.56
95-th percentile14223182.87
Maximum17577173.69
Range17575539.96
Interquartile range (IQR)7137604.822

Descriptive statistics

Standard deviation4348495.994
Coefficient of variation (CV)0.6338503115
Kurtosis-0.8259038793
Mean6860446.252
Median Absolute Deviation (MAD)3508038.04
Skewness0.2879996498
Sum6517423939
Variance1.890941741 × 1013
MonotonicityNot monotonic
2021-12-23T14:33:34.540754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
385232.921
 
0.1%
3171585.871
 
0.1%
7858868.921
 
0.1%
208842.461
 
0.1%
226891.061
 
0.1%
814826.121
 
0.1%
832841.171
 
0.1%
1405694.421
 
0.1%
1424061.951
 
0.1%
2006333.651
 
0.1%
Other values (940)940
98.9%
ValueCountFrequency (%)
1633.731
0.1%
6294.221
0.1%
9754.781
0.1%
9800.311
0.1%
9800.841
0.1%
9813.111
0.1%
9849.621
0.1%
9966.341
0.1%
10018.011
0.1%
10092.751
0.1%
ValueCountFrequency (%)
17577173.691
0.1%
17485573.051
0.1%
17470024.951
0.1%
17298505.871
0.1%
17085565.631
0.1%
17083278.631
0.1%
17054456.081
0.1%
16966176.511
0.1%
16941806.021
0.1%
16873033.811
0.1%

time_entladen_stark_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct597
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205013.9202
Minimum0
Maximum593181.15
Zeros40
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:34.675257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22304.7675
Q1123008.6775
median203374.52
Q3264311.3625
95-th percentile480312.196
Maximum593181.15
Range593181.15
Interquartile range (IQR)141302.685

Descriptive statistics

Standard deviation123505.3834
Coefficient of variation (CV)0.602424378
Kurtosis0.8246641979
Mean205013.9202
Median Absolute Deviation (MAD)69319.5
Skewness0.7630255465
Sum194763224.2
Variance1.525357972 × 1010
MonotonicityNot monotonic
2021-12-23T14:33:34.798572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040
 
4.2%
239704.044
 
0.4%
201767.842
 
0.2%
45712.612
 
0.2%
26248.562
 
0.2%
228812.912
 
0.2%
226026.612
 
0.2%
221762.482
 
0.2%
219154.112
 
0.2%
214002.422
 
0.2%
Other values (587)890
93.7%
ValueCountFrequency (%)
040
4.2%
1441.611
 
0.1%
1597.061
 
0.1%
1616.741
 
0.1%
17749.622
 
0.2%
19338.981
 
0.1%
21926.612
 
0.2%
22766.962
 
0.2%
23109.021
 
0.1%
23298.041
 
0.1%
ValueCountFrequency (%)
593181.151
0.1%
590747.871
0.1%
588001.681
0.1%
584509.31
0.1%
581060.041
0.1%
577898.061
0.1%
574226.151
0.1%
570478.331
0.1%
566288.941
0.1%
562124.131
0.1%

time_entladen_leicht_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1358084.162
Minimum7538.12
Maximum4347433.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:34.931612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7538.12
5-th percentile123876.6955
Q1468996.755
median1105874.61
Q31896081.998
95-th percentile3784764.458
Maximum4347433.06
Range4339894.94
Interquartile range (IQR)1427085.243

Descriptive statistics

Standard deviation1094643.005
Coefficient of variation (CV)0.8060200065
Kurtosis0.1013639948
Mean1358084.162
Median Absolute Deviation (MAD)679998.72
Skewness0.9663618627
Sum1290179954
Variance1.198243308 × 1012
MonotonicityNot monotonic
2021-12-23T14:33:35.058491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196178.51
 
0.1%
714809.151
 
0.1%
337253.441
 
0.1%
198416.221
 
0.1%
205627.71
 
0.1%
318912.521
 
0.1%
325908.511
 
0.1%
420498.371
 
0.1%
427212.081
 
0.1%
517643.841
 
0.1%
Other values (940)940
98.9%
ValueCountFrequency (%)
7538.121
0.1%
7539.591
0.1%
7542.951
0.1%
7552.61
0.1%
7610.031
0.1%
7616.331
0.1%
7629.051
0.1%
7634.981
0.1%
7661.041
0.1%
7665.271
0.1%
ValueCountFrequency (%)
4347433.061
0.1%
4343519.611
0.1%
4268724.251
0.1%
4264733.781
0.1%
4234224.361
0.1%
4200003.531
0.1%
4184108.451
0.1%
4179980.681
0.1%
4159345.361
0.1%
41559061
0.1%

time_laden_stark_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3649026.058
Minimum1627.08
Maximum10850409.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:35.184848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1627.08
5-th percentile180774.203
Q11667120.555
median3387895.06
Q35026416.302
95-th percentile8405080.343
Maximum10850409.36
Range10848782.28
Interquartile range (IQR)3359295.747

Descriptive statistics

Standard deviation2470853.712
Coefficient of variation (CV)0.6771269025
Kurtosis-0.3779777288
Mean3649026.058
Median Absolute Deviation (MAD)1671319.635
Skewness0.5570831566
Sum3466574755
Variance6.105118064 × 1012
MonotonicityNot monotonic
2021-12-23T14:33:35.314897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194718.281
 
0.1%
2211298.911
 
0.1%
4734429.791
 
0.1%
11155.931
 
0.1%
21971.541
 
0.1%
444310.171
 
0.1%
455328.221
 
0.1%
881343.111
 
0.1%
893002.11
 
0.1%
1333416.551
 
0.1%
Other values (940)940
98.9%
ValueCountFrequency (%)
1627.081
0.1%
6289.181
0.1%
9394.741
0.1%
9440.271
0.1%
9440.81
0.1%
9453.071
0.1%
9489.581
0.1%
9606.31
0.1%
9657.971
0.1%
9732.741
0.1%
ValueCountFrequency (%)
10850409.361
0.1%
10769780.971
0.1%
10468424.331
0.1%
10381041.461
0.1%
10306316.741
0.1%
10086358.661
0.1%
9995981.551
0.1%
9910653.221
0.1%
9763684.431
0.1%
9702559.451
0.1%

time_pause_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct798
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1050077.314
Minimum0
Maximum5585429.23
Zeros22
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:35.439723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16047.58
Q1129139.4
median648877.985
Q31504034.133
95-th percentile3759876.513
Maximum5585429.23
Range5585429.23
Interquartile range (IQR)1374894.733

Descriptive statistics

Standard deviation1183726.529
Coefficient of variation (CV)1.127275595
Kurtosis2.110042012
Mean1050077.314
Median Absolute Deviation (MAD)555902.795
Skewness1.575044181
Sum997573448.2
Variance1.401208495 × 1012
MonotonicityNot monotonic
2021-12-23T14:33:35.569736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
2.3%
30016
 
1.7%
14570.712
 
0.2%
16048.392
 
0.2%
16047.582
 
0.2%
28480.92
 
0.2%
43308.442
 
0.2%
54539.12
 
0.2%
68173.62
 
0.2%
79399.12
 
0.2%
Other values (788)896
94.3%
ValueCountFrequency (%)
022
2.3%
30016
1.7%
114001
 
0.1%
14570.712
 
0.2%
14574.912
 
0.2%
15817.742
 
0.2%
16046.882
 
0.2%
16047.582
 
0.2%
16048.392
 
0.2%
16065.262
 
0.2%
ValueCountFrequency (%)
5585429.231
0.1%
5470150.931
0.1%
5378402.951
0.1%
5338495.031
0.1%
5302217.341
0.1%
5261931.651
0.1%
5170176.721
0.1%
5130269.141
0.1%
5093991.631
0.1%
5053697.841
0.1%

time_temp_hoch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct358
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.097947
Minimum0
Maximum7730.64
Zeros593
Zeros (%)62.4%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:35.700274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34522.975
95-th percentile6904.285
Maximum7730.64
Range7730.64
Interquartile range (IQR)4522.975

Descriptive statistics

Standard deviation2664.734842
Coefficient of variation (CV)1.347801127
Kurtosis-1.11352209
Mean1977.097947
Median Absolute Deviation (MAD)0
Skewness0.764488618
Sum1878243.05
Variance7100811.776
MonotonicityNot monotonic
2021-12-23T14:33:35.835499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0593
62.4%
5552.061
 
0.1%
4684.591
 
0.1%
4439.531
 
0.1%
4436.761
 
0.1%
4376.341
 
0.1%
4364.011
 
0.1%
4495.31
 
0.1%
4513.651
 
0.1%
4784.881
 
0.1%
Other values (348)348
36.6%
ValueCountFrequency (%)
0593
62.4%
3001
 
0.1%
2695.91
 
0.1%
2700.841
 
0.1%
2731.811
 
0.1%
2735.551
 
0.1%
2802.391
 
0.1%
2826.011
 
0.1%
2940.841
 
0.1%
2967.321
 
0.1%
ValueCountFrequency (%)
7730.641
0.1%
7710.561
0.1%
7700.661
0.1%
7695.551
0.1%
7693.171
0.1%
7685.781
0.1%
7685.271
0.1%
7683.21
0.1%
7678.221
0.1%
7661.041
0.1%

time_temp_hoch_vorher
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct638
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2680278.352
Minimum0
Maximum11409748.64
Zeros33
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2021-12-23T14:33:35.971284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile139765.7
Q1793201.45
median1767303.76
Q33737098.882
95-th percentile8027307.819
Maximum11409748.64
Range11409748.64
Interquartile range (IQR)2943897.432

Descriptive statistics

Standard deviation2439237.488
Coefficient of variation (CV)0.9100687197
Kurtosis0.7340155128
Mean2680278.352
Median Absolute Deviation (MAD)1314359.26
Skewness1.205400973
Sum2546264435
Variance5.949879525 × 1012
MonotonicityNot monotonic
2021-12-23T14:33:36.102834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1578427.234
 
3.6%
033
 
3.5%
3739006.8214
 
1.5%
1341971.3812
 
1.3%
3393493.8510
 
1.1%
1497579.6310
 
1.1%
3038733.019
 
0.9%
1469602.179
 
0.9%
405011.468
 
0.8%
405611.668
 
0.8%
Other values (628)803
84.5%
ValueCountFrequency (%)
033
3.5%
17395.41
 
0.1%
17591.571
 
0.1%
17757.171
 
0.1%
17797.631
 
0.1%
18462.931
 
0.1%
18506.41
 
0.1%
18585.121
 
0.1%
21609.261
 
0.1%
35609.141
 
0.1%
ValueCountFrequency (%)
11409748.641
0.1%
10922820.591
0.1%
10890510.411
0.1%
10377187.111
0.1%
10346045.081
0.1%
10219232.411
0.1%
10186149.511
0.1%
9830754.111
0.1%
9798870.251
0.1%
9651188.081
0.1%

Interactions

2021-12-23T14:33:30.701847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:12.555640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.111087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.715765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.357576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.891944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.527636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.263922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.076844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.684114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.316552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.051557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.844389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:12.712657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.234224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.836655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.484433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.010952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.666086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.395662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.200941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.823416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.441605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.180713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.970720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:12.836702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.432452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.959525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.611510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.140940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.824699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.526194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.332065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.959834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.565632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.312183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.094254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:12.957721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.558465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.078254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.727981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.271956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.969226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.648721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.461131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.089025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.686823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.440706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.216484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.076776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.680319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.199451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.862270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.398811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.110042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.779619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.596155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.226186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.811598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.568753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.340317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.197812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.802336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.319061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.991355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.521957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.246165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.903019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.718682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.364916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.939144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.702061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.477966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.328472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:14.937263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.446767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.127163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.651987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.390697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:23.045660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.861296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.502612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:28.077908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:29.866841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.612205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.456721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.070993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.578200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.255810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:19.781600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.530616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:23.190705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:24.998905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.639069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:28.230337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.010788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.755361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.595165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.193747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.702991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.373518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.005666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.662942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:23.329394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.146949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.764179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:28.369369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.141779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:31.903602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.721685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.318913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:16.958177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.500866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.135048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.798039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:23.481206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.286004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:26.903505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:28.659003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.278145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:32.040070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.846612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.451107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.092213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.629815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.258183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:21.938333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:23.629555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.414961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.041606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:28.783162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.411972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:32.194543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:13.980709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:15.592140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:17.229928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:18.763455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:20.397954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:22.126834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:23.934381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:25.556983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:27.183300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:28.917552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-23T14:33:30.558214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-23T14:33:36.224510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-23T14:33:36.417315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-23T14:33:36.607265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-23T14:33:36.793697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-23T14:33:32.438283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-23T14:33:32.680690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

amperestundenzyklus_temperature_amaxtemperature_amintemperature_meantime_amintime_entladen_stark_vorhertime_entladen_leicht_vorhertime_laden_stark_vorhertime_pause_vorhertime_temp_hochtime_temp_hoch_vorher
02.000364323.5674218.3902521.798768385232.920.00196178.50194718.280.000.00.00
12.000250524.0663918.1540321.449898405533.190.00203379.98207816.730.000.00.00
21.958481100922.8189617.5713320.530506839519.2117749.62312669.57472156.6016048.390.0178418.56
31.963192101123.6453818.1225321.420698858525.6217749.62319737.63484111.8616048.390.0178418.56
41.911217192523.8948717.6658220.8394321248403.4736155.14406768.88743215.3328472.480.0344313.14
51.915461192723.3491217.5555920.9339731267739.9536155.14413665.08755670.7928472.480.0344313.14
61.869892282923.6453818.2485221.3556581655925.8153512.10507852.99991901.3543291.220.0508390.77
71.866606283122.6474417.8548120.9669771675254.4753512.10514573.531004497.9743291.220.0508390.77
81.826606378123.3179318.4060021.0186912135722.5771716.23607684.521325711.8755716.520.0716060.80
91.832734378323.6142018.9257021.7026102154750.8071716.23614282.981338163.6055716.520.0716060.80

Last rows

amperestundenzyklus_temperature_amaxtemperature_amintemperature_meantime_amintime_entladen_stark_vorhertime_entladen_leicht_vorhertime_laden_stark_vorhertime_pause_vorhertime_temp_hochtime_temp_hoch_vorher
9400.88583610150039.5280235.2150137.68045411671142.74311966.093883905.644709299.38964202.213189.268.559283e+06
9410.85918910150437.5956732.3242135.29166111699932.70311966.093886998.994727400.08971702.213093.358.587977e+06
9420.82418910451537.6884232.2005435.51837911926129.42314060.123952264.574823001.00994867.832967.328.775022e+06
9430.83732210451938.2449432.6952235.36495811956170.18314060.123955279.044842574.441002367.833014.478.805110e+06
9440.77836410753637.7038831.8140735.17433812177284.66315859.214018987.094929998.111029183.272802.398.985943e+06
9450.78493910754037.8893931.9686635.24565812207354.10315859.214021813.104949765.161036683.272826.019.016036e+06
9460.75977611055137.7966332.1077935.34340512427168.28317601.494080113.575040620.521059934.582735.559.190943e+06
9470.75874011055537.5338431.5976534.76850612457189.16317601.494082845.385060405.851067434.582731.819.220960e+06
9480.74877811357238.2913232.4015135.71939312672750.22319549.854142731.525144349.951094276.632695.909.394390e+06
9490.75020311357638.8632932.4633435.95851412703478.83319549.854145432.365164882.661101776.632700.849.425124e+06